Search Results for author: Kyle Genova

Found 17 papers, 7 papers with code

NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis

no code implementations14 Jul 2023 Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, Leonidas Guibas

This interaction field guides the sampling of an object-conditioned human motion diffusion model, so as to encourage plausible contacts and affordance semantics.

Motion Synthesis

Polynomial Neural Fields for Subband Decomposition and Manipulation

1 code implementation9 Feb 2023 Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie

We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.

Learning 3D Semantic Segmentation with only 2D Image Supervision

no code implementations21 Oct 2021 Kyle Genova, Xiaoqi Yin, Abhijit Kundu, Caroline Pantofaru, Forrester Cole, Avneesh Sud, Brian Brewington, Brian Shucker, Thomas Funkhouser

With the recent growth of urban mapping and autonomous driving efforts, there has been an explosion of raw 3D data collected from terrestrial platforms with lidar scanners and color cameras.

3D Semantic Segmentation Autonomous Driving +1

Multiresolution Deep Implicit Functions for 3D Shape Representation

no code implementations ICCV 2021 Zhang Chen, yinda zhang, Kyle Genova, Sean Fanello, Sofien Bouaziz, Christian Haene, Ruofei Du, Cem Keskin, Thomas Funkhouser, Danhang Tang

To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.

3D Reconstruction 3D Shape Representation

Differentiable Surface Rendering via Non-Differentiable Sampling

no code implementations ICCV 2021 Forrester Cole, Kyle Genova, Avneesh Sud, Daniel Vlasic, Zhoutong Zhang

We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement.

Inverse Rendering

IBRNet: Learning Multi-View Image-Based Rendering

1 code implementation CVPR 2021 Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.

Neural Rendering Novel View Synthesis

Local Deep Implicit Functions for 3D Shape

1 code implementation CVPR 2020 Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser

The goal of this project is to learn a 3D shape representation that enables accurate surface reconstruction, compact storage, efficient computation, consistency for similar shapes, generalization across diverse shape categories, and inference from depth camera observations.

3D Shape Representation Surface Reconstruction

Text-based Editing of Talking-head Video

1 code implementation4 Jun 2019 Ohad Fried, Ayush Tewari, Michael Zollhöfer, Adam Finkelstein, Eli Shechtman, Dan B. Goldman, Kyle Genova, Zeyu Jin, Christian Theobalt, Maneesh Agrawala

To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.

Face Model Talking Head Generation +2

Learning Shape Templates with Structured Implicit Functions

1 code implementation ICCV 2019 Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, Thomas Funkhouser

To allow for widely varying geometry and topology, we choose an implicit surface representation based on composition of local shape elements.

Semantic Segmentation

Unsupervised Training for 3D Morphable Model Regression

2 code implementations CVPR 2018 Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman

We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results.

Ranked #2 on 3D Face Reconstruction on Florence (Average 3D Error metric)

3D Face Reconstruction regression

Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes

no code implementations7 Apr 2017 Kyle Genova, Manolis Savva, Angel X. Chang, Thomas Funkhouser

We provide a search algorithm that generates a sampling of likely candidate views according to the example distribution, and a set selection algorithm that chooses a subset of the candidates that jointly cover the example distribution.

Semantic Segmentation

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